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Awesome Open Source

MobileNet-YOLO Caffe

A caffe implementation of MobileNet-YOLO detection network , train on 07+12 , test on VOC2007

Network mAP Resolution Download NetScope Inference time (GTX 1080) Inference time (i5-7500)
MobileNetV2-YOLOv3 71.5 352 caffemodel graph 6.65 ms 217 ms
  • inference time was log from script , does not include pre-processing
  • the benchmark of cpu performance on Tencent/ncnn framework
  • the deploy model was made by, set eps = your prototxt batchnorm eps
  • old models please see here

This project also support ssd framework , and here lists the difference from ssd caffe

  • Multi-scale training , you can select input resoluton when inference
  • Modified from last update caffe (2018)
  • Support multi-task model
  • pelee + driverable map


CNN Analyzer

Use this tool to compare macc and param , train on 07+12 , test on VOC2007

network mAP resolution macc param pruned IOU_THRESH GIOU
MobileNetV2-YOLOv3 0.707 352 1.22G 4.05M N N N
MobileNetV2-YOLOv3 0.715 352 1.22G 4.05M N Y Y
MobileNetV2-YOLOv3 0.702 352 1.01G 2.88M Y N N
Pelee-SSD 0.709 304 1.2G 5.42M N N N
Mobilenet-SSD 0.68 300 1.21G 5.43M N N N
MobilenetV2-SSD-lite 0.709 336 1.10G 5.2M N N N
  • MobileNetV2-YOLOv3 and MobilenetV2-SSD-lite were not offcial model

Coverted TensorRT models


Pelee-Driverable_Maps, run 89 ms on jetson nano , running project

YOLO Segmentation

How to use

Windows Version


Oringinal darknet-yolov3


test on coco_minival_lmdb (IOU 0.5)

Network mAP Resolution Download NetScope
yolov3 54.2 416 caffemodel graph
yolov3-spp 59.8 608 caffemodel graph

Model VisulizationTool

Supported on Netron , browser version

Build , Run and Training

See wiki

See docker

License and Citation

Please cite MobileNet-YOLO in your publications if it helps your research:

  Author = {eric612 , Avisonic , ELAN},
  Year = {2018}


Cudnn convolution


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